Mobile Device and Method and System for Transmission of Data thereto

ABSTRACT

A server system  150  for providing data to at least one mobile device  200,  the server system comprising a processing resource  174.  In a first embodiment, the processing resource  174  is configured to generate contour data representing a boundary of a respective area for which a weather-related parameter has substantially the same value, the contour data being sent to a mobile device  200.  In a second embodiment, the processing resource  174  is configured to determine a plurality of parameter values of a model representative of weather-related data, and to filter the parameter values such that only some of the values of sent to a mobile device  200.

FIELD OF THE INVENTION

The present invention relates to a mobile device and to a system andmethod for providing data to the mobile device.

BACKGROUND TO THE INVENTION

Portable computing devices, for example Portable Navigation Devices(PNDs) that include GPS (Global Positioning System) signal reception andprocessing functionality are well known and are widely employed asin-car or other vehicle navigation systems. Examples of known PNDsinclude the GO LIVE 1005 model manufactured and supplied by TomTomInternational B.V.

The utility of such PNDs is manifested primarily in their ability todetermine a route between a first location (usually a start or currentlocation) and a second location (usually a destination). These locationscan be input by a user of the device, by any of a wide variety ofdifferent methods, for example by postcode, street name and housenumber, previously stored “well known” destinations (such as famouslocations, municipal locations (such as sports grounds or swimmingbaths) or other points of interest), and favourite or recently visiteddestinations.

The PND determines the route based upon stored geographical data,usually in the form of a digital map. The stored geographical data caninclude data concerning a wide variety of features, for example theposition and characteristics of roads or other thoroughfares, theposition and characteristics of points of interest, and the position andcharacteristics of geographical features, for example rivers,coastlines, or mountains.

In operation, most PNDs periodically log their own position as afunction of time, for example every five seconds. PNDs can also logother, associated data such as speed or direction of travel as afunction of time. The data logged by PNDs or other portable computingdevices, can be referred to as probe data. It is known to obtain probedata from a large number of PNDs or other portable computing devices,and to process the probe data in order to verify or supplement existinggeographical data, for example existing digital maps.

Roads or other routes can be represented in a digital map by separatesegments. The digital map can include speed data that represents theexpected speed of travel over each segment of a road or other route.Such speed data is obtained from expected average travel speeds overroads of different types or is obtained from probe data representingactual travel of large numbers of vehicles over each road or other routein the digital map.

The speed data can be used in known systems to determine the fastestroute to a particular destination, to plan routes and/or to determine anestimated time of arrival (ETAs) at the destination. An example of asystem that uses speed data in such a way is the IQ Routes (RTM) systemproduced by TomTom International B.V.

Whilst speed data can be used to calculate preferred routes and ETAs,the accuracy of such calculations can be hindered due to theunpredictability of traffic. Speed profiles obtained from probe datausually represent long term averages, averaged over periods longer thanmany types of traffic fluctuations. Local short-lived events orfluctuations of traffic can invalidate, or render inaccurate, a specificspeed profile of a road segment. For example, one such event is badweather, which can easily double ETAs.

It is known, e.g. from US 2010/00161222, to transmit weather data to anavigation device, and to plan driving routes to avoid any region thathas a weather condition higher than a threshold value. However, thetransmission of weather data or weather-related data to PNDs isrestricted by the low bandwidth that is available for transmission ofdata to many such devices, and the large amount of data that is neededto adequately represent weather conditions over a large area.

Similarly, the transmission of weather data or other data representativeof the variation of a parameter as a function of location over a regionto other mobile devices, for example mobile phones or PNDs, isrestricted due to the large size of the data and the limited bandwidththat can be available in practice. The limited bandwidth makes itdifficult to provide for display of, for example, weather movies onPNDs, mobile phones or other mobile devices, particularly if the weatheror other data is updated on a regular basis.

SUMMARY OF THE INVENTION

In a first aspect of the invention there is provided a mobile devicecomprising a communication resource for receiving at least one data setthat represents a value of a parameter as a function of location; aprocessing resource for processing the at least one data set, whereinthe at least one data set comprises contour data that represents atleast one contour, the or each contour representing a boundary of arespective area for which the parameter has substantially the samevalue; and the processing resource is configured to process the contourdata to determine the value of the parameter for at least one location.

By receiving and processing contour data, the amount of data transmittedto the device can be reduced whilst retaining desired information. Thatcan be particularly valuable in the context of transmission of data tothe device via a bandwidth limited link, particularly if the data issubject to regular update.

The parameter may comprise a weather-related parameter, for examplerepresenting at least one of presence or amount of precipitation, forexample rain, hail sleet or snow; wind speed and/or direction; presenceor amount of lying snow; presence or amount of ice; level of visibility;presence or amount of fog; temperature; presence or absence of cloud.The use of contour data has been found to be particularly valuable inrelation to weather data.

The parameter may additionally or alternatively represent a speedmodification parameter representative of an expected modification inspeed due to weather or other environmental conditions. The speedmodification parameter can be used to modify an average speed of travelassociated with a road segment within a digital map.

The contour data may represent the or each contour as a polygon andoptionally, the contour data for the polygon comprises the co-ordinatesof the vertices or edges of the polygon. Alternatively the contour datacan represent the or each contour as any other suitable shape, forexample an ellipse.

The at least one data set may comprise a plurality of data sets, eachdata set representative of the value of the parameter as a function oflocation at a respective, different time, and each data set comprisingcontour data. The plurality of data sets may represent actual orforecast weather data at a plurality of different times.

Each data set may comprise at least one contour identifier, each contouridentifier identifying respective contour data, and optionally theprocessing resource is configured to determine using the at least onecontour identifier contour data in the or a second image data set thatcorresponds to contour data in the or a first image data set. Thus,contours can be tracked between data sets.

The processing resource may be configured to process each data set togenerate a corresponding image frame, wherein each image framerepresents the parameter as a function of location at a respective,different time.

The processing resource may be configured to provide the image frames toa display device for display of the image frames in sequence.

The parameter may comprise a weather-related parameter, and the imageframes may be for display as a weather movie.

The processing resource may be configured to interpolate contour datafrom a first of the image data sets representative of the value of theparameter as a function of location at a first time, and correspondingcontour data from a second of the image data sets representative of thevalue of the parameter as a function of location at a second, differenttime.

The processing resource may be configured to obtain, from theinterpolation, interpolated data corresponding to a third time betweenthe first time and the second time. The interpolated data may compriseor be used to obtain a further image frame representing the parameter asa function of location at the third time.

Alternatively, or additionally, the processing resource may beconfigured to extrapolate contour data for a first time to obtaincontour data for a further time.

The mobile device may comprise a mobile phone, a portable navigationdevice or a portable computer.

In a further aspect of the invention there is provided a server systemfor providing data to at least one mobile device, the server systemcomprising: a processing resource configured to obtain data thatrepresents the variation of a parameter as a function of location, andto process the data to generate at least one data set representative ofthe data for transmission to the at least one mobile device; and acommunications resource for transmitting the at least one data set tothe at least one mobile device, wherein: the processing resource isconfigured to determine from the data at least one contour thatrepresents a boundary of a respective area for which the parameter hassubstantially the same value, and to generate contour data representingthe at least one contour, the data set comprising the contour data. Thedata may comprise frame data and/or pixel data.

The parameter may comprise a weather-related parameter, for examplerepresenting at least one of presence or amount of precipitation, forexample rain, hail sleet or snow; wind speed and/or direction; presenceor amount of lying snow; presence or amount of ice; level of visibility;presence or amount of fog; temperature; presence or absence of cloud.

The parameter may additionally or alternatively comprise a speedmodification parameter representative of an expected modification inspeed due to weather or other environmental conditions.

The obtained data may represent the variation of the parameter as afunction of location at a plurality of different times, and theprocessing resource may be configured to process the data to obtain aplurality of data sets, each data set representing the variation of theparameter as a function of location at a respective, different time.

The processing resource may be configured to compare contour data for afirst one of the data sets representative of the value of the parameteras a function of location at a first time and a second one of the datasets representative of the value of the parameter as a function oflocation at a second time, and to identify contour data in the seconddata set representing a contour that corresponds to a contourrepresented by contour data in the second data set.

The processing resource may be configured to include contour identifiersin the data sets that identify the corresponding contours.

The processing resource may be configured to compare at least one ofshape, size, position or overlap of at least one contour of the firstdata set and at least one contour of the second data set to determinecorresponding contours.

The processing resource may be configured to determine expected movementor other change of the contours between the first time and the secondtime

The parameter may represent an environmental condition, for example aweather condition.

Each contour may represent a boundary of a respective area for which theenvironmental condition is substantially the same.

The processing resource may be configured to determine expected movementor other change of the contours between the first time and the secondtime based on predicted or actual wind speed and/or wind direction.

The processing resource may be configured to identify a contourrepresented by contour data in the first data set for which there is nota corresponding contour represented by contour data in the second dataset, to assign a location and/or time for the appearance ordisappearance of the contour, and to store an identifier representativeof the location and/or time of appearance or disappearance.

The identifier, for example a location identifier, may be stored in orwith the first or second data set.

The location of appearance or disappearance of the contour may bedetermined in dependence on expected movement or other change of thecontours between the first time and the second time.

The parameter may represent an environmental condition, for example aweather condition, each contour may represent a boundary of a respectivearea for which the environmental condition is substantially the same,and the processing resource may be configured to determine expectedmovement or other change of the contours between the first time and thesecond time based on predicted or actual wind speed and/or winddirection.

The processing resource may be configured to obtain the plurality ofdata sets, to determine changes in the contour data between data sets,and to select some of the data sets for transmission to the at least onemobile device in dependence on the determined changes between data sets.

The processing resource may be configured to omit a data set if changesin the contour data between the data set and a preceding data set, orbetween data set and a subsequent data set, are below a predeterminedlevel.

The processing resource may be configured to select data sets to ensurethat no contours both appear and disappear from the plurality of datasets at times between consecutive selected data sets.

In a further aspect of the invention there is provided a system forproviding datasets representative of the variation of a parameter withlocation, comprising at least one mobile device as claimed or describedherein and a server system as claimed or described herein.

In another aspect of the invention there is provided a method ofprocessing data at a mobile device, comprising receiving at least onedata set that represents a value of a parameter as a function oflocation, wherein the at least one data set comprises contour data thatrepresents at least one contour, the or each contour representing aboundary of a respective area for which the parameter has substantiallythe same value; and the method comprises processing the contour data todetermine the value of the parameter for at least one location.

The parameter may comprise a weather-related parameter, for examplerepresenting at least one of presence or amount of precipitation, forexample rain, hail sleet or snow; wind speed and/or direction; presenceor amount of lying snow; presence or amount of ice; level of visibility;presence or amount of fog; temperature; presence or absence of cloud.

The parameter may represent a speed modification parameterrepresentative of an expected modification in speed due to weather orother environmental conditions.

The contour data may represent the or each contour as a polygon andoptionally, the contour data for the polygon comprises the co-ordinatesof the vertices or edges of the polygon.

The at least one data set may comprise a plurality of data sets, eachdata set representative of the value of the parameter as a function oflocation at a respective, different time, and each data set comprisingcontour data.

Each data set may comprise at least one contour identifier, each contouridentifier identifying respective contour data, and optionally themethod comprises determining using the at least one contour identifiercontour data in the or a second image data set that corresponds tocontour data in the or a first image data set.

The method may comprise processing each data set to generate acorresponding image frame, wherein each image frame represents theparameter as a function of location at a respective, different time.

The method may comprise providing the image frames to a display devicefor display of the image frames in sequence. The parameter may comprisea weather-related parameter, and the image frames may be for display asa weather movie.

The method may comprise interpolating contour data from a first of theimage data sets representative of the value of the parameter as afunction of location at a first time, and corresponding contour datafrom a second of the image data sets representative of the value of theparameter as a function of location at a second, different time.

The method may comprise obtaining, from the interpolation, interpolateddata corresponding to a third time between the first time and the secondtime.

In another aspect of the invention there is provided a method ofproviding data to at least one mobile device, comprising obtaining datathat represents the value of a parameter as a function of location;processing the data to generate at least one data set representative ofthe data for transmission to the at least one mobile device, wherein theprocessing of the data comprises determining from the data at least onecontour that represents a boundary of a respective area for which theparameter has substantially the same value, and generating contour datarepresenting the at least one contour.

The data may represent the variation of the parameter as a function oflocation at a plurality of different times, and the method may compriseprocessing the data to obtain a plurality of data sets, each data setrepresenting the variation of the parameter as a function of location ata respective, different time.

The method may comprise comparing contour data for a first one of thedata sets representative of the value of the parameter as a function oflocation at a first time and a second one of the data setsrepresentative of the value of the parameter as a function of locationat a second time, and identifying contour data in the second data setrepresenting a contour that corresponds to a contour represented bycontour data in the second data set.

The method may comprise including contour identifiers in the data setsthat identify the corresponding contours.

The method may comprise comparing at least one of shape, size, positionor overlap of at least one contour of the first data set and at leastone contour of the second data set to determine corresponding contours.

The method may comprise determining expected movement or other change ofthe contours between the first time and the second time

The parameter may represent an environmental condition, for example aweather condition, each contour may represents a boundary of arespective area for which the environmental condition is substantiallythe same, and the method may comprise determining expected movement orother change of the contours between the first time and the second timebased on predicted or actual wind speed and/or wind direction.

The method may comprise identifying a contour represented by contourdata in the first data set for which there is not a correspondingcontour represented by contour data in the second data set, to assign alocation and/or time for the appearance or disappearance of the contour,and to store an identifier representative of the location and/or time ofappearance or disappearance.

The method may comprise determining the location of appearance ordisappearance of the contour in dependence on expected movement or otherchange of the contours between the first time and the second time.

The method may comprise obtaining the plurality of data sets,determining changes in the contour data between data sets, selectingsome of the data sets for transmission to the at least one mobile devicein dependence on the determined changes between data sets.

The method may comprise omitting a data set if changes in the contourdata between the data set and a preceding data set, or between data setand a subsequent data set, are below a predetermined level.

The method may comprise selecting data sets to ensure that no contoursboth appear and disappear from the plurality of data sets at timesbetween consecutive selected data sets.

In a further aspect of the invention there is provided a server systemfor providing data to at least one mobile device, the server systemcomprising: a processing resource configured to model a data setrepresenting variation of a variable as a function of location, themodelling comprising determining a plurality of parameter values of amodel that comprises at least one function; and a communicationsresource for transmitting parameter values to the mobile device.

The processing resource may be configured to combine a plurality of datasets to produce a combined data set, and the modelling may comprisemodelling the combined data set to determine the plurality of parametervalues.

Each data set may be a two-dimensional data set, the combined data setmay be a three-dimensional data set, and the modelling may comprisemodelling the three dimensional combined data set.

Each of the plurality of data sets may comprise a frame, for example animage frame, representative of the variation of the variable as afunction of location at a respective different time.

The or each data set may comprise weather-related data or otherenvironmental data, for example data representative of actual orexpected weather conditions as a function of location, the weatherconditions comprising, for example, one or more of presence or amount ofprecipitation, for example rain, hail sleet or snow; wind speed and/ordirection; presence or amount of lying snow; presence or amount of ice;level of visibility; presence or amount of fog; temperature; presence orabsence of cloud.

The data may comprise speed modification data representative of anactual or expected modification in speed. The three dimensional data setmay be representative of a weather movie.

The processing resource may be configured to perform a filtering processto reduce the number of parameter values to be transmitted to the mobiledevice.

The processing resource may be configured to select at least one of thedetermined parameter values and to omit the selected at least oneparameter value from the plurality of parameter values for transmissionto the mobile device.

The processing resource may be configured to compare the magnitude ofeach parameter value to a threshold and to select at least one parametervalue for omission in dependence on the comparison. The plurality ofparameter values may comprise a series of parameter values and theprocessing resource may be configured to truncate the series ofparameter values.

Modelling the data set may comprise fitting the data set to the at leastone function, and optionally the at least one function comprises atleast one Gaussian function.

The fitting may comprise fitting the data set to a linear superpositionof a fixed or variable number of Gaussian or other functions, forexample three-dimensional Gaussian or other functions. Each function maycomprise a multivariate Gaussian or other function with a fixed orvariable covariance matrix.

Alternatively or additionally the fitting may comprise fitting the dataset to at least one Fourier or wavelet function.

Modelling the data set may comprise performing a transform, andoptionally the transform comprises a Fourier transform or a wavelettransform.

The at least one parameter value may comprise a plurality of Fouriercoefficients or a plurality of wavelet coefficients.

The processing resource may be configured to select a range of values oflocation and to select data within that range of values of location forinclusion in the data set to be modelled.

The communications resource may be operable to receive, for example fromthe mobile device, location data identifying at least one location andthe processing resource is configured to select the range of values oflocation in dependence on the identified location.

The processing resource may be configured to select the range of valuesof location so that the at least one identified location is within therange. The location data may comprise route data representative of aroute to a destination. The processing resource may be configured toselect the range of values of location so that the route is within theselected range.

The processing resource may be configured to select the size of therange of values of position in dependence on a desired zoom level.

The communications resource may be configured to receive, for examplefrom the mobile device, zoom level data representative of the desiredzoom level.

The processing resource may be configured to model a plurality of datasets or combined data sets, each data set or combined data setrepresentative of the variation of a variable as a function of locationfor a respective, different area of a plurality of areas, thereby toobtain a set of parameter values in respect of each area.

The plurality of areas may comprise at least one sub-set of areas thattile a region.

The plurality of areas may comprise at least two sub-sets of areas, eachsub-set of areas tiling the region, and the size of the areas for afirst of the sub-sets of areas is different from the size of the areasfor the second sub-set of areas.

The processing resource may be configured to select at least one set ofparameter values for transmission to the mobile device in dependence onthe identified location and/or in dependence on the desired zoom level.

In a further aspect of the invention there is provided a mobile devicecomprising: a communication resource for receiving data; and aprocessing resource for processing the received data, wherein: thereceived data comprises a plurality of model parameter values and theprocessing resource is configured to process the model parameter valuesto determine the value of a location-dependent variable for at least onelocation and/or at least one time.

The processing resource may be configured to process the model parametervalues to generate at least one data set representing the value of thelocation-dependent variable for locations across an area, and optionallythe or each extracted data set comprises a frame, for example an imageframe.

The plurality of model parameter values may represent three dimensionaldata and the or each generated data set may be a two-dimensional dataset, for example a data set representing a slice through the threedimensional data.

The device may further comprise a display and the processing resourcemay be configured to generate an image or sequence of images on thedisplay from the data set or plurality of data sets. The sequence ofimages may comprise a movie, for example a weather movie.

The variable may comprise a weather-related variable, for examplerepresentative of expected weather conditions as a function of location,the weather conditions comprising, for example, one or more of presenceor amount of precipitation, for example rain, hail sleet or snow; windspeed and/or direction; presence or amount of lying snow; presence oramount of ice; level of visibility; presence or amount of fog;temperature; presence or absence of cloud.

The variable may be representative of an actual or expected modificationin speed due to weather conditions.

The processing of the parameter values by the processing resource maycomprise determining the value of at least one function using theparameter values, wherein optionally the at least one function comprisesat least one Gaussian function.

The processing of the parameter values by the processing resource maycomprise performing an inverse transform using the parameter values, andoptionally the inverse transform may comprise a Fourier transform or awavelet transform.

The communications resource may be operable to receive a plurality ofsets of parameter values. The processing resource may be configured toextract a respective data set from each set of parameter values and tocombine those data sets to produce a combined data set.

Each extracted data set may represent an image for a respectivedifferent area, and the combining of the data sets by the processingresource may comprise performing an image stitching process in respectof the images represented by the data sets.

In a further aspect of the invention there is provided a systemcomprising a server system as claimed or described herein and a mobiledevice as claimed or described herein.

In another aspect of the invention there is provided a method ofproviding data to at least one mobile device, comprising modelling adata set representing variation of a variable as a function of location,the modelling comprising using a model comprising at least one functionthereby to determine a plurality of parameter values; and transmittingthe determined parameter values to the mobile device.

In a further aspect of the invention there is provided a method ofobtaining determining location-dependent parameter data at a mobiledevice, comprising receiving data comprising a plurality of modelparameter values and processing the model parameter values to determinethe value of a location-dependent variable for at least one locationand/or at least one time.

In another aspect of the invention there is provided a computer programproduct comprising computer-readable instructions that are executable toperform a method as claimed or described herein.

There may also be provided an apparatus or method substantially asdescribed herein with reference to the accompanying drawings.

Any feature or aspect of the invention may be combined with any otherfeature or aspect of the invention, in any appropriate combination.Apparatus features may be applied as method features and vice versa.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the invention are now described, by way of non-limitingexample, and are illustrated in the following figures, in which:

FIG. 1 is a schematic illustration of a navigation system according toan embodiment;

FIG. 2 is a schematic illustration of a navigation device according toan embodiment;

FIG. 3 is a schematic illustration of a processing resource of FIG. 2;

FIGS. 4a and 4b are flowcharts illustrating in overview the processingof weather data at a server of the system of FIG. 1 according to a firstembodiment;

FIG. 5 is a plot showing two contours representing the same area ofprecipitation at different times;

FIG. 6 is a plot showing the emergence and disappearance of contours atdifferent times;

FIG. 7 is a flowchart showing in overview processes to determine ormodify an ETA or a route to a destination based on received contourdata;

FIG. 8 is a flowchart illustrating in overview a process for displayinga weather movie;

FIG. 9 is an image showing areas of a region for which a storm warningis current;

FIG. 10 is a flowchart illustrating in overview a mode of operation ofthe system of FIG. 1 according to a second embodiment;

FIG. 11a is a plot showing precipitation as a function of position at aparticular time;

FIG. 11b is a plot representing selected Fourier coefficients obtainedfrom a Fourier transform procedure performed on the data represented inFIG. 11 a;

FIG. 12a is a plot showing precipitation as a function of position,obtained following an inverse Fourier transform procedure performed onselected Fourier coefficients;

FIG. 12b is a plot representing the selected Fourier coefficients thatwere processed to obtain the plot of FIG. 12 a;

FIG. 13 is an image of a region showing sub-regions for which separateFourier transform procedures are performed;

FIG. 14 is an image of a region showing two, shifted sets ofsub-regions;

FIG. 15 is an image of a region showing three sets of sub-regions ofdifferent sizes; and

FIG. 16 is a flowchart showing in overview processes to determine ormodify an ETA or a route to a destination based on receivedcoefficients.

Embodiments described herein can be used to transmit data to mobiledevices in an efficient manner, and can be particularly useful in thecontext of the transmission of weather data to, and for example thedisplay of weather movies on, a mobile device. In a first embodiment,data is transmitted in the form of contour data, while in a secondembodiment data is firstly processed to determine a plurality ofparameter values of a model, and the parameter values or a subsetthereof are transmitted. Embodiments are not limited to the display ofweather movies, or the transmission of weather data, and can be used forthe transmission of any suitable type of data to any suitable type ofdevice. For example, a further embodiment is described herein in whichthe effect of weather conditions on expected speed of travel isdetermined and the data transmitted to the mobile devices comprisesspeed data which is used by the mobile devices to determine or modifyroutes to a destination and/or ETAs.

The first described embodiment, illustrated in FIG. 1, is directed tothe determination of the effect of weather conditions on expected speedof travel and the transmission of speed data in the form of contour datato PNDs, which use the speed data to determine or modify routes to adestination and/or ETAs. The generation and transmission of the speeddata is described but it will be understood that further embodiments arenot limited to the generation and transmission of speed data, but relategenerally to any type of contour data.

The system of FIG. 1 comprises a server 150 that is operable tocommunicate with a plurality of portable devices, for example PNDs 200 ato 200 e. Only five devices 200 a to 200 e are shown for clarity, but itwill be understood that in practice many thousands, or more, devices maybe in communication with the server 150.

In the embodiment of FIG. 1 the devices 200 a to 200 e arevehicle-mounted PNDs, that use Global Positioning System (GPS)technology to determine their positions, and that are able to performnavigation or mapping operations. The devices 200 a to 200 e are notlimited to being PNDs and may be any suitable type of device withnavigation functionality, for example a mobile phone or portablecomputer.

The server 150 includes a processor 154 operatively connected to amemory 156. In operation, software stored in server memory 156 is readby the processor 154 to load software modules or other softwarecomponents that enable the processor 154, to perform various processingor communication functions. In the embodiment of FIG. 1, the softwaremodules comprise a weather-dependent traffic modelling module 170 and aweather data processing module 172. The operation of the differentmodules will be described in more detail below.

The server 150 is further operatively connected to a mass data storagedevice 160. The mass storage device 160 contains a store of navigationdata, and can again be a separate device from the server 150 or can beincorporated into the server 150. The mass data storage device 160 canbe used to store probe data from the devices 200 a to 200 e.

The server 150 is also in operative communication with at least onesource of weather data 180, for example a third party website or weathercommunication centre that provides a dedicated weather feed. The atleast one source of weather data can, alternatively or additionally,comprise weather sensor(s), rain radar(s) or a computer performing modelcalculations. The server 150 communicates with the at least one sourceof weather data via any suitable communications channel, for example viaInternet connection or via a dedicated wired or wireless connection.

The server 150 is able to collect and fuse localized, accurate weatherinformation (including but not limited to current/forecast informationon precipitation, temperature, wind condition, and regionalsevere-weather warnings like storm or road ice, etc.) from multiplesources if desired.

The processor 154 is operable to transmit and receive information to andfrom devices 200 a to 200 e via communications channel 152, for examplevia transmitter 162 and receiver 164. The signals sent and received mayinclude data and/or other propagated signals. The transmitter 162 andreceiver 164 may be selected or designed according to the communicationsrequirement and communication technology used in the communicationdesign for the navigation system. Further, it should be noted that thefunctions of the transmitter 162 and receiver 164 may be combined into asingle transceiver.

In the normal course of operation of the navigation devices 200 a to 200e, GPS data from the devices are regularly recorded (for example, each 5seconds for some systems) as probe data on a logging device, usually inthe form of a data logger module included on the portable navigationdevices themselves.

As well as receiving and processing probe data received from the devices200 a to 200 e, the server 150 can also provide data to the devices 200a to 200 e, for example in the form of digital map data (for example,digital map data updated in view of received probe data), softwareupgrades, or traffic updates. It is a feature of the embodiment of FIG.1 that the server 150 also provides weather-related data to the devices200 a to 200 e, as will be described in more detail below. Theweather-related data can be used by the devices to for example, modifyan estimated time of arrival for a route in dependence on weatherconditions or modify a route based on expected variations of speed dueto weather conditions.

Although the communication channel 152 of the embodiment of FIG. 1 maycomprise an internet connection, any suitable form of data channel canbe used. The communication channel 152 is not limited to a particularcommunication technology. Additionally, the communication channel 152 isnot limited to a single communication technology; that is, the channel152 may include several communication links that use a variety oftechnology. For example, the communication channel 152 can be adapted toprovide a path for electrical, optical, and/or electromagneticcommunications. As such, the communication channel 152 includes, but isnot limited to, one or a combination of the following: electriccircuits, electrical conductors such as wires and coaxial cables, fibreoptic cables, converters, radio-frequency (RF) waves, the atmosphere, orfree space. Furthermore, the communication channel 152 can includeintermediate devices such as routers, repeaters, buffers, transmitters,and receivers, for example.

In one illustrative arrangement, the communication channel 152 includestelephone and computer networks. Furthermore, the communication channel152 may be capable of accommodating wireless communication, for example,infrared communications, radio frequency communications, such asmicrowave frequency communications. Alternatively or additionally, thecommunication channel 152 can accommodate satellite communication.

The communication signals transmitted through the communication channel152 include, but are not limited to, signals as may be required ordesired for given communication technology. For example, the signals maybe adapted to be used in cellular communication technology such as TimeDivision Multiple Access (TDMA), Frequency Division Multiple Access(FDMA), Code Division Multiple Access (CDMA), or Global System forMobile Communications (GSM). Both digital and analogue signals can betransmitted through the communication channel 152. These signals may bemodulated, encrypted and/or compressed.

A navigation device 200 in one embodiment is illustrated in FIG. 2. Thenavigation device 200 is representative of any of the devices 200 a to200 e shown in FIG. 1. It should be noted that the block diagram of thenavigation device 200 is not inclusive of all components of thenavigation device, but is only representative of many examplecomponents. The navigation device 200 is located within a housing (notshown). The navigation device 200 includes a processing resourcecomprising a processor 202, the processor 202 being coupled to an inputdevice 204 and a display device, for example a display screen 206.Although reference is made here to the input device 204 in the singular,the skilled person should appreciate that the input device 204represents any number of input devices, including a keyboard device,voice input device, touch panel and/or any other known input deviceutilised to input information. Likewise, the display screen 206 caninclude any type of display screen such as a Liquid Crystal Display(LCD), for example.

In one arrangement, the touch panel, and the display screen 206 areintegrated so as to provide an integrated input and display device,including a touchpad or touchscreen input to enable both input ofinformation (via direct input, menu selection, etc.) and display ofinformation through the touch panel screen so that a user need onlytouch a portion of the display screen 206 to select one of a pluralityof display choices or to activate one of a plurality of virtual or“soft” buttons. In this respect, the processor 202 supports a GraphicalUser Interface (GUI) that operates in conjunction with the touchscreen.

In the navigation device 200, the processor 202 is operatively connectedto and capable of receiving input information from input device 204 viaa connection 210, and operatively connected to at least one of thedisplay screen 206 and the output device 208, via respective outputconnections 212, to output information thereto. The navigation device200 may include an output device 208, for example an audible outputdevice (e.g. a loudspeaker). As the output device 208 can produceaudible information for a user of the navigation device 200, it isshould equally be understood that input device 204 can include amicrophone and software for receiving input voice commands as well.Further, the navigation device 200 can also include any additional inputdevice 204 and/or any additional output device, such as audioinput/output devices for example.

The processor 202 is operatively connected to memory 214 via connection216 and is further adapted to receive/send information from/toinput/output (I/O) ports 218 via connection 220, wherein the I/O port218 is connectible to an I/O device 222 external to the navigationdevice 200. The external I/O device 222 may include, but is not limitedto, an external listening device, such as an earpiece for example. Theconnection to I/O device 222 can further be a wired or wirelessconnection to any other external device such as a car stereo unit forhands-free operation and/or for voice activated operation for example,for connection to an earpiece or headphones, and/or for connection to amobile telephone for example, wherein the mobile telephone connectioncan be used to establish a data connection between the navigation device200 and the Internet or any other network for example, and/or toestablish a connection to a server via the Internet or some othernetwork for example.

Referring now to FIG. 3 of the accompanying drawings, internal flashmemory (not shown) of the device 200 stores a boot loader program thatis executable by the processor 202 in order to load an operating system250 and application software 252 from the storage device 214 forexecution by functional hardware components 254, which provides anenvironment in which the application software 252 can run. The operatingsystem 250 serves to control the functional hardware components andresides between the application software 252 and the functional hardwarecomponents 254.

The application software 252 provides an operational environmentincluding a GUI that supports core functions of the navigation device200, for example map viewing, route planning, navigation functions andany other functions associated therewith. The application software 252is able to plan routes and determine the expected time of arrival at adestination based on expected speed of travel for each segment of aroute, using known techniques. The expected speed of travel for eachsegment of road or other thoroughfares of a digital map can be stored asspeed data in the device 200 and accessed when required. The speed datacan be updated via speed data updates from the server 150.

When a user switches on the navigation device 200, the device 200acquires a GPS fix and calculates (in a known manner) the currentlocation of the navigation device 200. The user is then presented with adisplay showing in pseudo three-dimensions the local environment inwhich the navigation device 200 is determined to be located, and in aregion of the display below the local environment a series of controland status messages.

By touching the display of the local environment, the navigation device200 switches to display a series of virtual or soft buttons representedby icons by means of which a user can input a destination to which theywish to navigate, and perform various other functions relating tocontrol of the navigation device 200 or display of data on the display.

It is a feature of the embodiment of FIG. 1, that the functions of thenavigation device can be performed in dependence on weather-relateddata, for example received from the server 150. The application software252 includes a data processing module 260 that is operable to perform ormodify route calculation functions in dependence on receivedweather-related data, as described in more detail below.

Before describing the processing of the weather-related data at thedevices 200, the processing of weather data by the server 150 todetermine weather-dependent variations in traffic and the transmissionof speed or weather data to the devices 200 is described.

FIG. 4 is a flowchart illustrating in overview the processing of weatherdata at the server 150.

At the first stage 300 the server 150 obtains historical weather datarepresentative of weather conditions for a region represented by digitalmap over a significant period of time, for example one year. The server150 also has access to large quantities of probe data stored in the datastore 160 and representing the movement of vehicles over roads or otherthoroughfares represented by the digital map. The probe data can beprocessed to obtain, for example, one or more of the following: averagevehicle speeds along individual road segments per time bin; trafficdensity on individual road segments per time bin (obtained for examplefrom extrapolated and normalised density of navigation devices or fromultrasound distance detectors located at the front and back ofvehicles); statistical spread of speeds for each road segment and timebin; jam probabilities for individual road segments and time bins.

The weather data can represent one or more different weather types, forexample one or more of: presence or amount of precipitation such asrainfall, hail or snow; windspeed; wind direction; presence or amount ofice or lying snow; presence or amount of surface water; presence oramount of fog; visibility level; temperature. The weather data obtainedfrom any suitable source, for example official weather recordsmaintained by government or commercial agencies. Weather data frommultiple sources may be used. Weather-related data obtained fromsuitably equipped individual vehicles can also be used in someembodiments. Such weather-related data obtained from individual vehiclescan include for example data representative of slip events, operation offog lamps, operation of windscreen wipers, temperature measurements, orrain detection.

The server 150 has access to a sufficiently large archive of weatherdata and probe data to allow statistically significant measurement ofweather dependent speed averages and their significance. The modellingmodule 170 at the server 150 is configured to apply, at stage 302,correlation techniques to correlate variation in average speed fordifferent segments of roads or other thoroughfares of the digital mapwith the variation in the or each weather condition. The modellingmodule 170 can also derive statistical correlations between weatherconditions and, for example, jam probabilities, drivers' routepreferences or any other traffic-related parameter if desired. Theserver 150 provides offline statistical analysis for calculating speedprofiles and weather influence.

It has been found that a classification of different road types based ontheir sensitivity to weather conditions is useful in performing thecorrelation procedure. For example, precipitation intensity alone isoften not sufficient as a parameter within some algebraic expressiongiving the average speed modification for all roads. The influence ofprecipitation can be non-linear and discontinuous in some cases,depending on the local traffic scenario and road layout.

In one mode of operation each road segment is classified to one of aplurality of road type classifiers (for example, 5, 10 or more differentroad type classifiers may be used). Each classifier may represent roadshaving one or more characteristics, for example width, number ofcarriageways, surface type, average speed or traffic volume under normalconditions, urban or rural location, proximity to traffic junction ortraffic lights. Functional road classifications (FRCs) such as thoseused by TomTom International B.V. in their products may be used as theroad classifiers. Each road segment may be assigned a classifier basedon a priori assessment of properties of the road segment or,alternatively, each road segment may be assigned a classifier based on aposteriori determination of how speed properties of the segment varywith weather conditions. In that second case, each classifier mayrepresent a respective level of sensitivity to at least one weathercondition.

The number and types of classifications that are used can depend on thenumber of distinct reactions to weather conditions from differentsegments and the importance to traffic, and also on the limits ofband-width for transmission of data to the devices 200 a to 200 e viachannel 152, as in general the larger the number of classifications thelarger the amount of data that needs to be transmitted to the devices.In one mode of operation, classification data representative of theclassification of each road segment is stored as digital map data at thedevices 200 a to 200 e.

For each classification, the reaction to different types and levels ofweather conditions can be determined by correlating speed data for eachsegment of that classification with corresponding weather datarepresentative of weather conditions at the road segment. Usually, thereaction represents modification, usually reduction, of speed withrespect to a weather-unimpaired speed profile. Congestion estimationscan usually be inferred via the time-dependent speed profile as adeviation of free-flow speed. The correlation process can be atime-dependent process, which correlates weather data and speed dataobtained for the same time of day or week, thus taking into accountexpected daily or weekly variations in average speed for particularsegments.

The correlation process can be used to determine average speeds as afunction of precipitation and/or road condition, average speeds specificfor FRC or other classifications under the weather condition, jamprobabilities under the weather condition, and/or a classification ofweather types with respect to average speed.

The correlation process results in the generation of weather reactiondata for each road segment classification type representing the expectedmodification of average speed in response to weather conditions of oneor more types and levels of severity.

It has been found that the variation of speed with severity of weatherfor each road segment classification type can be modelled effectivelyusing a quantitative model, for example an exponential model in whichthe speed modification varies exponentially with the severity of aparticular weather condition. The weather reaction data can be fitted tothe selected model using known fitting techniques. Stages 302 to 304 arecomputationally costly and, in some modes of operation, are repeatedonly every 3 to 6 months or whenever a traffic engineer or otheroperator considers that changes that affect the results may haveoccurred.

It is a feature of the described embodiment that speed modificationfactors for each road classification type and each location in a regioncan be calculated on-the-fly based on received weather data, as will nowbe described in more detail with reference to FIG. 4 b.

In operation the server 150 receives current weather data at stage 306from the weather data source 180 on a regular basis, for example every15 minutes. The current weather data usually comprises a set of weatherdata comprising the most recent measured weather data and sets offorecast weather data representing forecast weather conditions for timesin the future, for example at 15 minute intervals for three hours in thefuture.

Each set of weather data may comprise a plurality of data points, eachdata point representing a weather condition (for example rainfall level)at a respective location in a region. In some cases, the data points maycorrespond to regularly spaced positions across the region. In someembodiments, the weather data is in the form of text data or XML data,although any suitable data format can be used. In some cases, the set ofweather data comprises or is used to generate a frame of weather datathat can be used to display an image representing the weather conditionfor the region at a particular time.

In some modes of operation the server 150 may receive a plurality ofsets of weather data each representing a different weather condition(for example rainfall, temperature, wind speed) at one particular time.

The weather data processing module 172 processes each received set ofweather data for a selected weather type at stage 308 based upon thepreviously determined weather reaction data to determine a respectivespeed modification factor due to the actual or forecast weathercondition at a particular time for each location in the region and foreach road classification type. The speed modification factor for aparticular road classification type and that location represents theexpected modification of expected average speed of travel along a roadof that road classification type at that location due to the weathercondition.

Further sets of speed modification factor data may be produced for eachweather type that is under consideration. In some modes of operationonly a single weather type is considered, for example rainfall. Ifmultiple weather types are considered, then the speed modificationfactor data for the different weather types may be combined, for exampleby taking the highest calculated speed modification factor for eachlocation.

In the mode of operation that has been described, the reaction of a roadsegment, or a road segment type, to weather conditions is determinedbased upon correlation of historical probe data and historical weatherdata. Expected speed modification can then be determined for routesegments for the current time and for future times, based upon thecurrent or forecast weather and the historically-determined reaction ofthe road segments to weather conditions.

In some cases, the historical correlation takes into account short-termvariations of weather conditions over time and associated changes inspeed or other travel conditions.

For example, historical correlations can be used to determine theexpected time for speeds for particular segments or road types torecover to normal following the end of a particular weather condition,for example following rainfall or snowfall. Such time-dependenthistorical correlations can be used to determine expected speedmodification.

For instance, weather conditions may now be good or be expected to begood for a particular segment but if there has been rainfall or snowfallwithin a certain preceding period of time, it may be determined based onhistorical data that there is expected to be a speed modification,usually a speed reduction.

Such calculations can take into account a large number of differentparameters. For example, the time taken for speeds to recover to normalfollowing rainfall, snowfall, ice formation or other weather condition,may depend on how long the weather condition lasted, the severity of theweather condition, and other weather conditions in the interveningperiod since the weather condition ended. For example, the time take forspeeds to recover to normal following snowfall may depend on theseverity and length of the snowfall and on temperatures in theintervening period since the end of the snowfall. Such calculations canalso depend on road classification type. For example, major roads may befound to recover more rapidly from snowfall or other conditions, whichmay be due to a variety of factors, for example the condition of theroad surface, the amount of use of the road, and the increasedlikelihood that the road will be cleared by a snow plough or other roadclearance equipment.

At the next stage 310, speed modification factor data is provided to oneor more of the navigation devices 200 a to 200 e. In one mode ofoperation, the navigation device 200 transmits to the server 150 itslocation and route data representative of a selected route. The server150 selects speed modification factor data for the immediatesurroundings of the navigation device 200 and speed modification factordata for locations along the selected route and within a certain marginaround it. The margin can be directionally dependent and can be adaptedintelligently based on factors like the current wind speed and directionand on the actual or expected travelling speed of the device 200. Thetransmitted data comprises speed modification factor data to the currenttime as well as forecast speed modification factor data for a feasibleperiod into the future. In combination, this enables the device 200 totake into account the relevant weather conditions along the individualsegments of the route at the actual times when the vehicle in which thedevice 200 is installed will pass them, as will be described in moredetail below.

In some variants of the embodiment, the server 150 selects speedmodification factor data for transmission to the device 200 based onboth the location of the device 200 and the route, and also on theexpected time at which the device 200 will arrive at different segmentsof the route. Thus, for example, speed modification factor data for thecurrent time for locations within a margin around the current locationof the device 200 would be transmitted, but speed modification factordata for a time in the future for those locations may not be transmittedas the device 200 would be expected to be at a location further down theroute by that future time.

By selecting only some of the speed modification factor data fortransmission based on the location of the device 200 and/or the routethe amount of data that needs to be transmitted can be reduced.

A further significant feature of the embodiment is that the speedmodification factor data is processed further before transmission by theweather data processing module 172 in order to reduce the amount of datato be transmitted. The processing of the data in one mode of operationcomprises representing the speed modification factor data by contourdata and transmitting the contour data rather than the raw speedmodification factor data to the device 200.

The weather data processing module 172 processes a set of speedmodification factor data for a particular time and a particular roadtype classification to determine contours that represent the boundariesof areas which the speed modification factor has the same value. Thecontours may be nested contours, with one contour falling within anothercontour and delimiting the transition from an area having a speedmodification factor of one value to an area having a speed modificationfactor of another value.

The processing module 172 then fits each contour to a shape and storesthe data representative of the fit as contour data. The fitting can beperformed using any suitable known fitting techniques, for exampleleast-squares fitting. Any suitable shape can be used, but it has beenfound particularly efficient to fit each contour to a polygon shape.Alternatively, ellipse or rounded triangle shapes can be used, forexample. The number of vertices of each polygon can be fixed in advanceor can be selected during processing to ensure that a goodness of fitwithin a predetermined threshold is obtained.

The contour data comprises the coordinates of each of the vertices ofthe fitted polygon, together with a contour value representing the valueof the speed modification factor represented by the contour.

Although the fitting of contours to the data has been described inrelation to speed modification factor data, in other embodiments orvariants, the contour fitting is performed on each set of weather datarather than on the speed modification factor data. The resulting weathercontour data can then be processed, if desired, to determine speedmodification factor contours using the speed modification model.

The processing of the sets of data to convert them into contour data isuseful not just for the determination of speed modification factors, butalso, for example for the efficient transmission of any suitably type ofposition-dependent data, for example weather-related data or otherenvironmental data, such as weather data or speed modification factordata, to the devices 200 a to 200 e over a bandwidth limitedcommunication path, for example for purposes of displaying weathermovies on the devices.

Precipitation data, for example, typically can be represented as amoving and changing landscape of contour levels. The transmission ofentire JPEG code frames for the purposes of display of weather data on amobile device is wasteful since current radar technology allowsresolution of only up to around 10 different precipitation levels. Bytransmitting contour data, for example denoting regions of equalprecipitation, rather than raw weather data to bandwidth limited orprocessing capacity limited mobile devices the effective transmission ofweather data for display, including weather movies, becomes feasible.The weather data can be used by mobile devices to display cloud,precipitation or other weather maps and movies. The mobile devices canalso process the data to extract information or make qualifieddecisions.

In other embodiments, it is contemplated the weather data that isreceived by the server 150 comprises movie frames representingpixel-based images and the processing module 172 transforms thepixel-based images into contour polygons representing a reduced set ofrelevant precipitation levels. The contour data representative polygons(for example 2-D points representing longitude and latitude) can betransmitted to the mobile device.

It is a feature of the embodiment that, as well as generating contourdata to represent data, for example weather-related data such as speedmodification factor data or weather data, the processing module alsotracks contours over time.

The processing module 172 performs the contour identification andfitting procedure for a plurality of datasets, each datasetrepresentative of a different time, and then performs a furtherprocedure to track contours between the different datasets. The trackingprocedure comprises comparing the shape, size and position of contoursbetween different datasets to determine which contours in the differentdatasets correspond to each other and for example represent the samearea of weather at different times. Any suitable data comparison,correlation or fitting procedure can be used to determine which contourscorrespond to each other.

By way of illustration, two contours 400 and 402 representing the samearea of precipitation at different times, and obtained from differentweather datasets, are shown in FIG. 5. The positions of the contours400, 402 in the figure represent the relative positions of the areas ofprecipitation. The first contour 400 represented by an identifier values=0 is representative of an area of precipitation at a time A. Thesecond contour 402 represented by an identifier value s=1 isrepresentative of the area of precipitation at a later time B.

The contour data representing the contours 400, 402 can be interpolatedto obtain the shape and position of the contour at an intermediate timeC=A+(B-A)/2 by interpolating the positions of corresponding vertices ofthe contours as illustrated schematically in FIG. 5. A linearinterpolation of the vertices is indicated in FIG. 5 by dotted lines.

It can be seen in FIG. 5 that the shape of the contour has changedbetween time A and time B, with an additional vertex 404 towards the topleft of the contour having appeared at time B, and a vertex 406 towardsthe bottom of the contour having disappeared between time A and time B.

In order to enable easier subsequent interpolation between the contourdatasets 400, 402 the processing module 172 artificially adds contourdata points representative of the appearing and disappearing vertices tothe contour datasets.

The processing module can also include vertex identifiers in the contourdatasets to identify corresponding vertices between the differentcontour datasets. For example, a vertex identifier identifies thatvertex 406 for the contour dataset at time A corresponds to artificiallyadded vertex 410 for the contour dataset at time B.

In some cases, a contour will disappear or appear between one frame orother dataset and the next frame or other dataset. The emergence ordisappearance of contour can usually be detected by the failure of theprocessing module 172 to find a corresponding contour in the immediatelyfollowing or preceding frame or other dataset.

In one mode of operation, a position of emergence or disappearance isassigned to the contour in the immediately following or preceding frameor other dataset. The position of emergence or disappearance can bedetermined from the expected movement of contours between datasets, forexample determined from actual measured wind speeds or from thedetermined movement of other contours between the frames or otherdatasets.

An illustration of the emergence and disappearance of contours isprovided in FIG. 6, which shows contours present in a current frame(time, t=0) and in a previous frame (time, t=−1). There are two contours500, 502 representative of areas of precipitation present in a previousframe, at time t=−1. In the current frame, one of the contours 502 hasdisappeared and a new contour 504 has appeared. Contour 500 is presentin the current frame as contour 508 although its shape has changed. Thecontour has moved due to the prevailing wind conditions.

In this case, the processing module 172 firstly finds correspondencesbetween contours of the different frames by using a local wind vectorrepresentative of the measured wind speed and associates contours thatoverlap best. Thus, in FIG. 6 it is identified that contour 500corresponds to contour 508 and represents the same area ofprecipitation.

Next, the processing module 172 determines that any leftover contours(in either frame) have appeared or disappeared. The location X of originof contour 504 at time t=−1 is determined by following the local windvector one frame interval backwards. Likewise, the location Y ofdisappearance of contour 502 is determined by following the local windvector one frame interval forwards. Data identifying the locations X andY are included in the frames or other datasets for transmission to themobile devices.

It is a feature of the embodiment of FIG. 1, in certain modes ofoperation, that the frames or datasets to be transmitted are selected toensure that no contours both appear and disappear in between consecutiveframes or other datasets. In such modes of operation, a plurality offrames of the datasets are processed, and a subset of those frames orother datasets are selected for transmission, thus reducing thetransmission load. By selecting the frames for transmissionappropriately it can be ensured that no significant contour featureswill be omitted entirely from the transmitted data, and that allsignificant contour features appear in at least one frame or otherdataset, whilst also reducing the transmission load.

It is possible that contour 500 corresponds to contour 504 rather thancontour 508. However, it is more plausible the contour 500 correspondsto contour 508 because they overlap best if transposed by the local windvector, as determined by processing module 172. It is possible that lessobvious situations may arise, in which there is ambiguity as to whichcontours correspond to each other. However the system only requires aplausible solution. If the alternatives are completely equivalent, forexample the overlap similarities equal in all respects, then thecorrespondence could be chosen randomly as either choice would beequally consistent with radar or other weather measurements.

The processing module 172 is able to perform processes such asidentification of corresponding contour shapes in different frames,emergence or disappearance of vertices and contours, changes in contourtopology (for instance splitting or merging contours) for example byintelligently selecting contour datasets, consistently defining contoursand storing or transmitting information on the correspondence betweenvertices and contours in adjacent frames or other datasets. Byperforming such processes at the server 150 the processing burden at thenavigation devices 200 a to 200 e or other mobile devices can bereduced.

At the next stage of the process, the selected weather-related data, forexample speed modification factor data or weather data is provided tothe device 200. The transmission of speed modification factor data andsubsequent processing of that data at the device 200 to determine anestimated time of arrival (ETA) and/or to determine or modify a route toa destination will now be described. The transmission of weather dataand the processing of the weather data by the device 200 to display aweather movie will then be described.

FIG. 7 is a flowchart showing in overview processes performed by thedevice 200 to determine an ETA and/or to determine or modify a route toa destination based on received weather-related data.

At the first stage 600 the process, the device 200 receives currentweather-related data. The current weather-related data in this casecomprises a plurality of speed modification factor datasets. Each speedmodification factor dataset comprises a set of speed modification factordata points, each set of data points corresponding to a particular timeand to a particular road type classification. A separate speedmodification factor dataset is transmitted for each time (for example,the current time and for three hours into the future at 15 minuteincrements) and for each road type classification.

The transmitted speed modification factor data represents expected speedmodifications due to current weather conditions as well as due to theforecast weather for a feasible period into the future. This informationenables the device to take into account relevant weather conditionsalong individual segments of the route at the actual times when thevehicle will pass them.

In practice, the speed modification factor datasets may not varysignificantly between each predetermined interval, and in that case theprocessor 150 may transmit only updates to the previously transmitteddatasets. For example, the processor 150 may only transmit new datasetsfor a particular time if the speed modification factor data for thattime has altered by greater than a predetermined threshold since theprevious transmission.

In the process of FIG. 7, as discussed, each received speed modificationfactor dataset comprises contour data, comprising vertex coordinatesthat represents the positions of vertices of contours representing areasof equal speed modification factor, contour identifiers identifyingcontours that represent areas of the same speed reduction factor, andvertex identifiers enabling the tracking of the corresponding contoursbetween different datasets representing different times.

Furthermore, in the example described in relation to FIG. 7, contourdata is only transmitted to the device 200 for regions within apredetermined margin of the route to a selected destination that hasbeen calculated by the device 200. The device 200 has previouslytransmitted destination data, or route data representative of the route,to the server 150.

It can be understood that, by using contour data, by transmitting datafor locations only within a predetermined margin distance of acalculated route, and by transmitting updated data only when required,the rate of data transmission to the device 200 can be kept within theavailable bandwidth. In variants of the described embodiment, thosefeatures can be provided alone or in any suitable combination.

The device 200 has already calculated a route to a selected destinationusing known techniques. The route comprises a plurality of connectedsegments and for each of those cases segments the device 200 determinesan expected ETA at the segment, based on expected speeds of travel foreach preceding segment of the route. The calculation of ETAs can beperformed using speed data stored at the memory 214 of the device 200using known techniques, such as those used in the TomTom IQ Routes (RTM)system.

The memory 214 of the device 200 also stores road type classificationdata, classifying each of the segments represented in the digital map toone of the classifications used by the server 150 to determine reactionto weather conditions.

The device 200, for each of the segments, reads classification data fromthe memory 214 to determine the road classification for that segment andthen determines the received speed modification factor dataset that isrelevant for that segment based on the calculated ETA at the segment. Inone mode of operation, the device 200 determines the speed modificationfactor dataset that is representative of a time closest to the ETA atthe segment, and uses that speed modification factor dataset for thatsegment.

In another mode of operation, the device 200 determines the speedmodification factor datasets that immediately precede and immediatelyfollow the ETA at the segment. The processing module 260 then performsan interpolation procedure to interpolate the position of correspondingcontours represented in those preceding and following datasets. Forexample, the processing module 260 determines corresponding contourvertices in the preceding and following datasets using the vertexidentifiers, and then performs a linear interpolation of the coordinatesfor those contour vertices.

The device 200 then determines a modified speed of travel for thesegment by multiplying the stored expected speed of travel for thatsegment by the speed modification factor for that segment determinedfrom the received weather-related data and representing the expectedmodification of speed of travel due to adverse weather conditions.

The device 200 then recalculates the ETA for the next segment of theroute based on the modified expected speed of travel determined for thepreceding segment or segments, and determines the expected speedmodification for that next segment. The process is repeated for eachsegment of the route in succession.

At stage 604, once the process has been repeated for each segment of theroute, the processing module 260 determines an ETA at the finaldestination. The ETA may then be displayed to the user. In some modes ofoperation, a message, or icon or other feature is also output to theuser indicating that the ETA has been modified due to adverse weatherconditions. The map display may indicate the intersection of the currentroute with the bad weather zone (which may, for example, be coloured redor any other suitable colour) plus the new route (which may be coloureddifferently to the existing route, for example coloured green).

In some modes of operation, the device 200 also determines whether torecalculate the optimum route to the destination, in view of expectedspeed modifications due to adverse weather conditions.

In normal operation the device 200 calculates many possible routes to adestination (depending on the destination and the number of routesavailable) and select the fastest route (that route providing theearliest expected ETA). In one mode of operation, the device 200determines other possible routes to the destination periodically, as thevehicle travels along the selected route, and determines the expectedETA and time of travel to the destination for each of those routes undernormal conditions.

If the modified ETA for the selected route is determined to be laterthan the ETAs under normal conditions for the other routes by apredetermined margin (for example, the ETA is more than 5 minutes or 10minutes later, or the expected time of travel is more than 5% or 10%later), then the processing module 260 performs the expected speedmodification procedure for one or more of the other possible routes todetermine the expected ETA for those other routes. If theweather-modified expected ETA for one of the other routes is earlierthan for the selected route then the selected route may be replaced bythat other route that has an expected ETA (dependent on any routerestrictions specified by the user) or a message may be displayed to theuser giving them the option to switch to that other route.

In another mode of operation, the weather-modified expected speed oftravel and ETA is performed for each of the possible routes each timenew weather-related data is received.

In the described embodiment, the speed modification data comprises speedmodification factor data that can be multiplied by the expected speed oftravel under normal conditions to obtain expected speed of travel underthe associated weather condition. However the speed modification datacan be in any suitable form in alternative embodiments. For example, thespeed modification data may represent an expected absolute modificationof speed, or an expected absolute speed, under the associated weathercondition.

In some cases, the received weather-related data indicates that asegment of a route is impassible, or may be impassible at some futuretimes. In that case, the processing module 260 may exclude such segmentsfrom route calculations, and the selected route would avoid suchsegments.

In the described embodiment, the processing module 260 can excludeparticular regions (not only single segments) from the route calculationif it is determined that severe weather conditions (for example,flooding or tornadoes) are current or expected. In some variants, theprocessing module 260 excludes certain road types (for example, minorroads) or provides less weighting to such road types in routecalculations, if it is forecast that particular weather conditions mayoccur (for example, lying snow). Thus, major roads may be favoured ifthere is, for example, lying snow in anticipation that major roads maybe subject to better or faster maintenance (for example, road clearing).In some case, particular roads or road types may be excluded ordowngraded depending on the season as well as actual or forecast weatherconditions. For example, in some countries, dry river beds may be usedas roads in summer but may be expected to be impassible in winter.

Particular road classification types may alternatively or additionallybe excluded from, or given less weighting, in route calculation or ETAcalculation, in the case of actual or forecast weather conditions of apredetermined type or severity. For example, minor roads may be excludedor given less weighting in route calculations in the presence ofparticular actual or forecast weather conditions, for example snow.Thus, a vehicle may be routed away from minor roads to major roads inthe case of actual or forecast snow even if the fastest route undernormal conditions may be via minor roads. Similarly, in the presence ofsnow or other such weather conditions, a route calculation may maintaina route on major roads even if a traffic jam is known to be present onmajor roads which, under normal weather conditions, would cause arerouting to minor roads.

The process of FIG. 7 has been described in relation to the receipt andprocessing of speed modification factor data. In other embodiments, thedata transmitted from the server 150 is data representing the value ofany other suitable parameter as a function of position, for exampleenvironmental data such as weather data that represents actual and/orexpected weather conditions. Again, the data can be transmitted ascontour data. However, in this mode of operation the speed modificationfactors are calculated at the device 200 from the weather data by theprocessing module 260. The process for calculating the speedmodification factors from the weather data corresponds to that processwhen performed by the server.

It can be understood that in the described embodiments, effectivemodification of ETA and/or route selection based upon expected weatherconditions can be achieved whilst also maintaining the rate of datatransmission to the device 200 within available bandwidth.

The device 200 can also be used to display movies or other images basedon the received data, for example weather movies. In embodiments fordisplaying weather movies, the received data comprises weather datainstead of or as well as speed modification factor data. One suchembodiment is described with reference to FIG. 8, which is a flowchartillustrating in overview a process for displaying a weather movierepresenting, in this case, rainfall. Any other position dependentparameter, for example any other weather condition, or combination ofweather conditions, can be represented in alternative embodiments ormodes of operation.

At the first stage 700 of the process contour datasets are receivedwhich represent measured and/or predicted rainfall level for a series oftimes, for example times at 15 minute increments covering a three-hourperiod. As already described, each contour dataset can comprise vertexcoordinates and contour identifiers representing contours that representareas of the same rainfall level. Contours can be nested one withinanother, to represent increasing levels of rainfall.

The processing module 260 processes each contour dataset to generate aframe of image data, for example a frame of JPEG or MPEG data, byin-filling each of the contours represented by the contour dataset withthe rainfall level represented by the contour. In the case of nestedcontours, the processing module 260 in-fills each contour up to theboundary of a contour contained within it.

The device 200 is then able to display the frames of image data usingknown techniques. The frames of image data can be displayed insuccession thereby to display a weather movie representing, in thiscase, the variation with time of measured and/or forecast rainfall levelacross a region.

In a variant of the embodiment illustrated in relation to FIG. 7,successive contour datasets are interpolated using an interpolationprocedure to obtain intermediate contour data representative of rainfallat an intermediate time, and that intermediate contour data is alsoprocessed by the processing module 260 to generate a corresponding imageframe. Thus, successive image frames or movies with time resolutiongreater than that of the received contour datasets can be obtained. Theinterpolation procedure may be the same as or similar to that alreadydescribed in relation to the interpolation of speed modification factorcontour datasets. For example, linear interpolation of contour vertexcoordinates can be used to determine the position of contour vertices atthe intermediate times.

In other embodiments, the contour datasets represent areas for which aweather warning or other localised weather condition information hasbeen issued. In some such embodiments, a fixed fragmentation of an area(for example a country or continent) into meteorologically meaningfulregions (also referred to as natural regions) that are expected, onaverage, to exhibit similar or homogeneous weather conditions isdetermined and stored at the server 150.

In such embodiments, data representing the fragmentation of the area isalso stored at the device 200 in the form of labelled contour data, forexample labelled polygon definitions comprising, for instance, vertexcoordinates.

In operation, upon receipt of weather warning or other weather conditiondata, the server 150 transmits only the labels of the currently affectedregions along with the respective weather conditions specification (forexample, an identifier specifying that the weather warning relates toheavy rain, strong wind or other weather condition). A unique identifiermay be assigned to a particular weather warning. When the weatherwarning becomes obsolete, the server 150 is able to transmit acorresponding message indicating that the weather warning represented bythe identifier is no longer current.

In an alternative embodiment, the server 154 determines whether the sameweather warning or other weather condition applies to multiple adjacentnatural regions. The server then determines contour data representativeof the outline of the adjacent natural regions merged together andtransmits the contour data to the device 200. The device 200 thendetermines from the received contour data the area to which the weatherwarning applies.

In a further alternative embodiment, the server decides on-the-fly foreach area to which the weather warning or other weather conditionapplies whether it is more efficient (for example requires transmissionof less data) to provide fixed region labels identifying the naturalregions making up the area to which the warning or condition applies, orto provide contour co-ordinate data representing the area. The latter islikely to be more efficient when a large number of natural regions forman area with a simple shape. For example, in FIG. 9 a storm warning iscurrent for areas 800, 802, 804 and 806 of Germany. In this case,contour coordinates are transmitted to the device 200 representing area800 which comprises a large number of contiguous natural regions,whereas natural region labels are transmitted to the device 200representing areas 802, 804 and 806 (and other similar regions) whichare made up of a smaller number of natural regions.

The embodiment of FIG. 1 has been described in relation to thegeneration, transmission and processing of weather-related data, forexample weather data such as rainfall data or speed data representingthe variation of expected speeds with weather conditions. However,embodiments are not limited to the generation, transmission andprocessing of such weather-related data. Any suitable type of data maybe processed to produce contour data for transmission and processing.The use of contours can be particularly valuable in relation to positiondependent parameter data in which areas of a region have substantiallythe same parameter value. Examples of such parameters include, forexample, weather data as already described, other environmental datasuch as pollution level, sea temperature, presence or absence of oil,gas mineral or other deposit, presence or absence of vegetation,presence or absence of buildings and other map data. It can beparticularly useful to model using contours parameters that are subjectto regular change, particularly if it is necessary to transmit dataconcerning the parameters over a bandwidth limited link, as the use ofcontours provides an efficient way of reducing the amount of data to betransmitted whilst retaining desired information.

The embodiment of FIG. 1 has so far been described in relation to thegeneration, transmission and use of contour data. It is alsocontemplated, however, in other embodiments of the invention that data,such as weather data or other environmental data, which isrepresentative of the variation of the value of a variable with locationcan be processed at the server 150 to determine a plurality of parametervalues of a model. The parameter values or a subset thereof, rather thanthe modelled data itself, are then transmitted to the mobile devicesthus again reducing the amount of data transmitted to the mobile devices200 a -200 e. The application software 252 at the mobile device 200, insuch embodiments, can include a processing module, e.g. similar tomodule 260 (shown in FIG. 3), for obtaining the values of the variableor variables from the received parameter values.

Operation of this embodiment is illustrated in overview in the flowchartof FIG. 10.

At the first stage 910 of the process, the server 150 receives currentweather from the weather data source 180 as described above in relationto stage 306 of FIG. 4b . In the process described, the weather data israinfall data, although any other type of weather data can be used.

At the next stage 912, the weather processing module 172 performs atransform or fitting thereby to represent the received weather data as aplurality of parameter values. Any suitable fitting or transform can beused, depending on properties of the data, as will be described in moredetail below. For example, in the mode of operation now described inrelation to FIG. 10, a Fourier transform is used.

The processing module 172 determines the number of data sets, T,included in the received current weather data. Each weather data setcomprises an array P of data points of size m x n.

The processing module 172 stacks the arrays P of the data sets to athree-dimensional array PP. The stacking of the arrays produces athree-dimensional array PP that represents complex, non-convex shapes ofequal precipitation levels. As we are dealing with time and space thedata can be reinterpreted as three-dimensional volumetric discrete data,for example a cube with values f(x,y,z). Optionally, the processingmodule 174 performs a smoothing process on the three-dimensional arrayPP to ensure sufficiently smooth level transitions from onetwo-dimensional array P to the next two-dimensional array P.

Next, the processing module 172 performs a fast Fourier transform (FFT)procedure, thereby to fit the three dimensional data to a set of Fouriercoefficients. The FFT procedure can be performed using standard Fourierlibrary functions. In alternative embodiments any suitable Fouriertransform, or indeed any other transform or fitting procedure can beused. Embodiments are not limited to the use of FFTs. For example, adiscrete n-dimensional Fourier transform may be used.

The output of the FFT procedure is a set of Fourier coefficients thatrepresents the three-dimensional data set PP comprising the weather dataas a function of time and position. The set of Fourier coefficients canbe used to reconstruct the set of weather data, and to extract weatherdata for any selected time or position.

The processing module 172 then performs a filtering process in which itselects some of the Fourier coefficients and discards other of theFourier coefficients. In one mode of operation the processing module 172selects those coefficients c that have a magnitude greater than athreshold value. In another mode of operation the processing module 172selects coefficients for the first W of the N terms of the Fouriertransform, where W is a threshold value. In a further mode of operation,an optimisation process is performed to select those coefficients thatcan best represent the data, subject to a constraint on the number ofcoefficients and/or on the quality of representation of the data. Anyother suitable filtering or selection process can be used.

By way of illustration, FIG. 11a is a plot of a single data set showingprecipitation as a function of position at a single time. The plotincludes contours that represent different rainfall levels. The FFTprocedure is performed on the data set and FIG. 11b is a plotillustrating those Fourier coefficients that have been selectedfollowing the filtering process. In this case, all Fourier coefficientsare selected and effectively no filtering has been performed.

FIG. 12b is a plot illustrating those Fourier coefficients that havebeen selected following a further filtering process to select only thosecoefficients that have a magnitude greater than 2.8. It can be seen fromFIG. 12b that a large number of the coefficients have been discarded,and in this case the filtering provides a compression ratio of 8.72.

FIG. 12a is a plot of precipitation data extracted by performing aninverse FFT using only the selected coefficients. It can be seen thatthere are some minor differences between the original data set of FIG.11 a and the reconstructed data set of FIG. 12a . For example, there aresome slight ripples in one of the contours of FIG. 12a that are notpresent in the corresponding contour of FIG. 11a . However, for thepurposes of, for example, displaying weather movies or providing weatherdata for traffic or individual purposes, small distortions present inthe reconstructed data are negligible and the Fourier transform andfiltering processes do not produce any significant losses. The exampleof FIGS. 11 and 12 relates to a two-dimensional data set, for clarity ofdisplay of the results. However, similar results are obtained forthree-dimensional data sets.

The selected Fourier coefficients are then, at stage 914, transmitted toone or more mobile devices, for example the portable navigation device200. The filtering process to be used to select coefficients fortransmission to the devices can be selected in dependence on theproperties of the data in question and/or in dependence on the availablebandwidth for transmission to the devices. For example the number ofcoefficients that are selected for transmission can be varied independence on the available bandwidth.

At the next stage, the Fourier coefficients are received at thenavigation device 200. The processing module 260 performs an inverse FFTto extract three-dimensional weather data representing rainfall as afunction of position and time. Known library functions or routines canbe used to perform the inverse FFT. The processing module 260 is able toextract weather data for any selected position or time from the receivedFourier coefficients, not only for the times represented by the originalweather data sets.

In the process of FIG. 10, the processing module 260 uses the inverseFFT procedure to extract a series of data sets, each data setrepresenting rainfall as a function of position across the region at arespective time. The extraction of each data set can be considered asthe taking of a slice through a three-dimensional data set. Theprocessing module 260 is then able for example, to generate an imageframe from each extracted data set. The image frames can be used by thedevice 200 to display a weather movie represented by the image frames onthe display 206 in accordance with known techniques.

The use of the Fourier transform process, and the filtering or otherselection of the coefficients, results in a significant reduction of theamount of data that is needed to be transmitted to the mobile devices,for example portable navigation devices, to represent weather data onthose devices, for example in the form of weather movies. For manyapplications that require transmission of weather-related data to mobiledevices, the exact value of the weather data at any particularindividual pixel may not be critical. Thus, considerable truncation orother filtering of the coefficients can be performed without significantloss in performance.

The process of FIG. 10 is described in relation to the transmission ofrainfall data, but the process can be used to transmit anyweather-related or environmental data or indeed any two-dimensional orthree-dimensional data set having suitable properties.

The Fourier transform and filtering process has been described withreference to FIG. 10 in relation to single sets of weather datarepresenting the weather for an entire region (for example across anentire continent) at a particular time. In practice, a user of thenavigation device or other mobile device may only need to receiveweather data for an area in the vicinity of the location of the deviceor, for example, for an area around a planned route. Therefore, in someembodiments separate Fourier transform and filtering processes areperformed on subsets of the weather data comprising weather data forselected sub-regions

FIG. 13 is an illustration showing nine different sub-regions 930 a-930i. A separate Fourier transform and filtering process is performedseparately on weather data for each of the sub-regions, to produce ninedifferent sets of selected Fourier coefficients. The location of aportable navigation device 200 is also indicated in FIG. 13. Theportable navigation device 200 transmits its location to the server 150and the server 150 selects the set of Fourier coefficients for thesub-region 930 e in which the device is located and transmits thoseFourier coefficients to the device 200. Weather data can be extracted bythe device 200 from the received Fourier coefficients as alreadydescribed. By transmitting only Fourier coefficients for a sub-region orsub-regions of relevance to a particular device the amount of datatransmitted to the device can be reduced still further.

In practice, in the example described in relation to FIG. 13, the server150 would in one mode of operation transmit four sets of Fouriercoefficients, for sub-regions 930 d, 930 e, 930 g, 930 h, as the device200 is close to the boundary of those four sub-regions. Althoughtransmission of the four sets of Fourier coefficients can still providea reduction in the amount of data transmitted to the device 200, thecombining of the extracted weather data at the device 200 can becomputationally cumbersome in some circumstances.

In practice the server 150 may be dealing with thousands or millions ofactive devices spread over the Earth. However, in the situationillustrated in FIG. 13 each set of Fourier coefficients representweather data for only a single sub-region. Fourier coefficients for morethan one sub-region may need to be transmitted if the device is close toa boundary between sub-regions. Fourier coefficients for many moresub-regions may need to be transmitted if the user of the device wishesto view or otherwise use weather data for a region larger than a singleone of the sub-regions. Furthermore, to reduce the amount of data to betransmitted requires a sufficiently dense tiling of the sub-regions, butthe smaller the size of the sub-regions the greater the probability thatmultiple neighbouring sets of weather data or images must be combined atthe device 200, which can be computationally cumbersome as discussed.

Operation of a further embodiment is described in relation to FIG. 14.In this case, the region is divided into two sets of sub-regions. Eachset of sub-regions provides a tiling of the region, but each set isshifted against the other set by half of the length of a sub-region in xand y directions (or longitudinal and latitudinal directions). In FIG.14, two sets of sub-regions 930 a-930 i and 940 a-940 i are shown.

In operation of the embodiment described in relation to FIG. 14, thedevice 200 again provides its location to the server 150. The server 150then selects a sub-region within which the device 200 is located. Therewill be two sub-regions within which the device is located, in this casesub-regions 930 e and 940 d. The server 150 then selects the one ofthose two sub-regions for which the device is most distant from theboundary of the sub-region, in this case sub-region 940 d. The server150 then transmits the selected Fourier coefficients for the selectedsub-region 940 d to the device 200. The server 150 also transmits datarepresenting the position, shape and size of the selected sub-region, oran identifier of the selected sub-region 940 d enabling the navigationdevice 200 to determine the position, shape and size of the sub-region940 d. The device 200 is able to extract weather data for the sub-region940 d using an inverse FFT as already described.

In the embodiment of FIG. 14, the sub-regions of the different tilingsare shifted by half a linear dimension (length or width) of a sub-regionwith respect to each other. However any suitable shift sizes can beused. For example in some embodiments there are three tilings, withsub-regions of each tiling being shifted by one third of a lineardimension of the sub-region. The pre-calculation of different sets ofFourier or other coefficients for different sub-regions for each updateof weather information is readily achievable, given the computationalpower and memory available at the server 150.

The choice of the size of the sub-regions can be made by an operator atthe server 150. The size of the sub-regions can also be referred to as azoom level. Clearly, once the weather data is extracted at the device200, the user at the device 200 can choose to display a subset of theextracted data, for example to zoom in further.

Different users often have different zoom level requirements. Forexample, a user of a device 200 may wish to view or otherwise useweather data for a continent or country as a whole, or may wish to viewor otherwise use weather data for a specific region around the device200. In some embodiments the amount of data to be transmitted to thedevices 200 can be reduced by performing the Fourier transform andfiltering or other selection processes at the server 150 for sub-regionsof different sizes (and thus for different zoom levels). An example ofthe operation of such an embodiment is illustrated in FIG. 15.

In FIG. 15, a region is tiled with different sets of sub-regions, eachset of sub-regions having sub-regions of different sizes/different zoomlevels. In the case of FIG. 15, there are three separate tilings eachwith sub-regions of different sizes. The three separate tilings arerepresented by sub-regions 950 a-950 i, 952 a-952 i and 954 a-954 i. Notall of the sub-regions of the tilings are shown, for clarity.

The server 150 determines the location of each device 200 and the zoomlevel required by that device 200, usually from data received from thedevice 200. In some cases a default zoom level may be set for eachparticular device, for example based on device type or previous userrequests or settings for that device. The server 150 then selects thesub-region that most closely matches the device location and zoom leveland transmits the selected Fourier coefficients for that sub-region tothe device 200. The server 150 may also transmit a sub-region identifierand/or data representing the size and shape of the sub-region. Thedevice 200 is able to extract weather data using an inverse Fouriertransform procedure as already described.

In some cases, the server 150 may select a sub-region having a size/zoomlevel slightly smaller than that requested by the device 200 in order toavoid transmitting coefficients data for more than one sub-region.

In further embodiments, the server 150 calculates and selects Fouriercoefficients for sub-region tilings having different sizes/zoom levelssuch as those described in relation to FIG. 15 and for shifts of thosesub-region tilings such as those described in relation to FIG. 14. Theserver 150 selects the best sub-region fora particular device 200 basedon the position of the device and/or requested zoom level from amongstthe different tilings.

In alternative modes of operation, the server 150 may select asub-region in respect of which to transmit Fourier coefficient data to aparticular device based on data other than the current location of thedevice 200. For example, a user of the device 200 may request, viadevice 200, the data for another location than that at which the deviceis currently located. Alternatively or additionally, the server 150 maytransmit coefficient data for sub-regions covering all locations along,and optionally within a predetermined margin of, a route to a selecteddestination of the navigation device 200.

In other alternative embodiments, below a certain density of datarequests in time and space, coefficient sets are calculated individuallyfor each device 200, on demand. This can give optimal data compression.In such embodiments the server 150 must be able to calculate, select andtransmit the Fourier coefficients for each region or sub-regionindividually requested by devices within the period before the nextweather update. If the server 150 is not able to achieve that targetthen it becomes more efficient to compute Fourier coefficients forpredetermined tilings as already described. In such embodiments, theserver 150 can switch between calculation of coefficients individuallyfor each requesting device and calculation of coefficients forpredetermined tilings in dependence on the level of demand. The level ofdemand is monitored periodically or continuously by the server 150. Forregions where consistently few devices are present (for example centralAustralia, or Siberia) it is possible that on-demand calculation ofcoefficients will always be used.

Operation of this embodiment has been described with reference to theprocessing and transmission of weather data to the device 200. However,any suitable type of data, may be processed and transmitted. In someembodiments, the Fourier transform and selection processes areperformed, for example, on speed modification factor data rather thanraw weather data. In such embodiments, the device 200 is able to extractthe speed modification factor data for a relevant sub-region orsub-regions by applying an inverse Fourier transform process. The speedmodification factor data can then be used by the device 200, for exampleas discussed above in relation to FIG. 7, to determine or amend ETAs orroutes.

In one mode of operation, server 150 determines Fourier coefficients forsets of speed modification factor data for different areas that a tile aregion, in similar fashion to that described in relation to weatherdata. The navigation device 200 transmits to the server 150 its locationand route data representative of a selected route. The server 150selects one or more sets of Fourier coefficient data for transmission tothe device 200 that correspond to areas that encompass the immediatesurroundings of the navigation device 200 and locations along theselected route and within a certain margin around it. The margin can bedirectionally dependent and can be adapted intelligently based onfactors like the current wind speed and direction and on the actual orexpected travelling speed of the device 200.

FIG. 16 is a flowchart showing in overview processes performed by thedevice 200 to determine an ETA and/or to determine or modify a route toa destination based on received coefficients representative ofweather-related data.

At the first stage 1000 the process, the device 200 receives theselected Fourier coefficients representing a combined, three dimensional(time and position in x and y directions) speed modification factor dataset for each road classification type. The device 200, for each of thesegments, reads classification data from the memory 214 to determine theroad classification for that segment and then performs an inverseFourier transform procedure on the appropriate received set of Fouriercoefficients to extract the speed modification factor data sets for thatclassification for one or more times.

The device 200 has already calculated a route to a selected destinationusing known techniques. The route comprises a plurality of connectedsegments and for each of those cases segments the device 200 determinesan expected ETA at the segment, based on expected speeds of travel foreach preceding segment of the route. The calculation of ETAs can beperformed using speed data stored at the memory 214 of the device 200using known techniques, such as those used in the TomTom IQ Routes (RTM)system.

The memory 214 of the device 200 also stores road type classificationdata, classifying each of the segments represented in the digital map toone of the classifications used by the server 150 to determine reactionto weather conditions.

The device 200, for each of the segments, reads classification data fromthe memory 214 to determine the road classification for that segment andthen determines the speed modification factor that is relevant for theposition and ETA at that segment, by reading the speed modificationfactor from the extracted speed modification factor data sets.

The device 200 then, at stage 1002, determines a modified speed oftravel for the segment by multiplying the stored expected speed oftravel for that segment by the speed modification factor for thatsegment determined from the received coefficients and representing theexpected modification in speed of travel due to adverse weatherconditions.

The device 200 then recalculates the ETA at the next segment of theroute based on the reduced expected speed of travel determined for thepreceding segment or segments, and determines the expected speedmodification for that next segment. The process is repeated for eachsegment of the route in succession.

At stage 1004, once the process has been repeated for each segment ofthe route, the processing module 260 determines an ETA at the finaldestination. The ETA may then be displayed to the user. In some modes ofoperation, a message, or icon or other feature is also output to theuser indicating that the ETA has been modified due to adverse weatherconditions. The map display may indicate the intersection of the currentroute with the bad weather zone (which may, for example, be coloured redor any other suitable colour) plus the new route (which may be coloureddifferently to the existing route, for example coloured green).

In an alternative mode of operation, the inverse Fourier transformprocedure is performed to extract speed modification factor data onlyfor particular specified times and positions corresponding to segmentsof the route, rather than performing the inverse Fourier transformprocedure to extract entire speed modification factor data sets.

As will be appreciated, the embodiment of FIG. 16 can include any or allof the optional features described in relation to FIG. 7. For example,the received coefficients may be used to exclude or decrease weightingapplied to one or more types of road segments.

In the above described embodiments, the processor 150 performs a Fouriertransform process to represent the three-dimensional data using Fouriercoefficients, and the device 200 performs an inverse Fourier transformprocess to extract data from the selected Fourier coefficients.

The use of a Fourier representation can be particularly suitable as theoriginal three-dimensional function can be approximately reconstructedto a sufficient degree by only a subset of the original Fouriercoefficients. In some modes of operation, based on the assumption thatthe original function is sufficiently smooth and therefore effectivelyband limited, it is possible to retain only the coefficients up to amaximum frequency in order to yield the desired compression.

The Fourier representation has the advantage of providing a completeorthonormal basis of the function space and involving well-establishedmathematics. The transformation of a function into Fourier coefficientsis straightforward and is guaranteed to yield well-defined results forall well-behaved and non-pathological cases. Furthermore, the assumptionof being effectively band limited applies to a rather general range ofreasonably smooth functions rendering this approach feasible for a broadspectrum of potential applications.

The features of the Fourier transform approach that make it particularlysuitable for many applications also apply to wavelet transforms, and inalternative embodiments or alternative modes of operation the server 150uses a wavelet transform process rather than a Fourier transform processto determine the coefficients. Similarly the device 200 in suchembodiments or modes of operation is configured to perform an inversewavelet transform to extract the weather-related data or other data fromthe selected coefficients.

Alternative embodiments use transforms, fittings or other parametricrepresentations instead of Fourier wavelet transforms, to obtainparameter values that represent the location-dependent variable orvariables. More specific prior knowledge of the characteristics of thefunction to be compressed motivate different representations that canallow for an even stronger compression for the purposes of a particularapplication. Targeting a more specific type of function, and alternativeparametric representations need not be complete, i.e. it does not haveto be able to construct every possible function to infinite precision.Rather, it is sufficient in practice that the typically expected targetfunctions for a particular application can be approximated to therequired degree. Furthermore, the parametrization does not have to belinear as in the Fourier case as long as feasible approaches for thetransformation to and from the parameter space are available.

For example, in some embodiments or modes of operation the volumetricdata is modelled by linear superposition of a fixed and finite set of3-D Gaussian functions with different, but fixed, covariance matrices.For approximate reconstruction, only the scale weights andthree-dimensional positions for a small subset of these bases functionsare used. Alternatively, other suitable smooth volumetric functions canbe used in place of Gaussian functions. An important aspect of thisapproach is that since the composition is linear, finding the optimalrepresentation (for example using a least square error approach) of theoriginal function can be achieved with the given set of basis functionscan readily be obtained using straightforward linear algebramethodology.

Furthermore, reducing the complexity of such approximation can beachieved using standard methods for principal component analysis (PCA)such as the singular value decomposition (SPD) approach (thiscorresponds to applying a threshold to the coefficients in the Fourierbased approach). Depending on the characteristics of the typical targetfunction, a large library of basis functions may be required in order toachieve a desired accuracy. An advantage of the approach is that areconstruction of the approximate function by the device 200 merelyneeds to linearly combine the (spatially translated) values of a fixedset of basis functions that can for example be stored in the form ofsimple lookup tables. The computational complexity of the reconstructionperformed at the client device 200 is thus low.

In a further alternative embodiment, the volumetric data can be modelledby linear superposition of a variable number of 3-D Gaussians ofvariable covariance matrix and position, whereby the number, covariancematrices, and positions of the Gaussians are selected to optimallymatched the individual function at hand. In comparison to the embodimentdescribed in the preceding paragraph the achievable accuracy of therepresentation is not limited by the choice of a finite set of basisfunctions. More precisely, since the number, positions and covariancematrices of the Gaussians are optimised for the given volumetric data,the accuracy of the approximation can be expected to be better and thenumber of Gaussians required is likely to be smaller than for theembodiment of the preceding paragraph. However, finding the optimalrepresentation of a given target function according to the scheme ismathematically more involved, since the model is nonlinear in thecoefficients of the covariance matrices and depends on the number ofGaussians. Nevertheless, since the problem is similar to, for example,the modelling of multimodal probability density in the field ofstatistical data analysis, numerous approaches from that area can beadapted for this application, such as for example the mean shiftapproach to finding a suitable number of Gaussians (cluster centres) anda modified k-means algorithm for determining the optimal positions ofthe Gaussians (cluster centres). Furthermore, the class ofexpectation-maximisation (EM) algorithms provides a general fameframework for performing parameter fits of the envisaged kind, and otheriterative, nonlinear optimisation algorithms are applicable as well infurther embodiments.

In contrast to the Fourier or wavelet transform approach, a furtherreduction of the representation's parametric complexity cannot easily beachieved a posteriori. Instead a limitation on the number of Gaussiansallowed to model the original data is imposed during the fitting step.In the case of reconstruction of the data from the functions, the device200 is configured to compute the values of Gaussian functions withvariable covariance. The computational complexity of the reconstructionis thus higher than is the case for embodiments that use Gaussianfunctions with fixed covariance.

In some embodiments the server 150 and the device 200 are configured touse any of the described approaches for transforming or fitting thedata, and for subsequently extracting the data. The approach to be usedcan be selected either manually or automatically in dependence onproperties of the data and/or in dependence on which approach providesthe best fit.

As will be appreciated, the above described embodiments are able toreinterpret the time domain as a third dimension and treat the availabletwo-dimensional frames or other data sets as slices through athree-dimensional volumetric function, and to represent that volumetricfunction in a parametric form that allows the definition of a suitableapproximation of the original data with lower parametric complexity.

The more one knows about characteristics or empirical behaviour of thedata and its typical use the better the compression that can beobtained. Several aspects can be considered, for example datagranularity; data alphabet; Shannon entropy of atomic components; whatrepresentation allows the most optimal choice of a basis; is it worthdefining a global basis and only transferring parameter values (thebasis can then be as large as desired) or should the basis betransferred along with data (basis should then be optimal in size); canthe basis be computed or even represented as a mathematical object; isthe computation fast enough, does it require too much memory; what isthe ratio of computational power between server and device; must thedata be transferred losslessly or not. The combined compression of twochunks of data always improves the compression. An exception to thatrule occurs in the case of compression of random and statisticallyindependent data. The term basis is meant in the mathematical sense. Therotation of a matrix to approximate a representation in m principalcomponents (if possible) reduces the data from n² to mn components. Asmall random matrix cannot be compressed in the average. A sufficientlylarge random matrix can be compressed to its eigenvalues (reduction fromn²to n components) since the entire information about the statisticalproperties is completely coded in the eigenvalues. A similar approach istaken in the Fourier or wavelet transform of wave-like or sufficientlysmooth discrete data which can often be found in images. Wave-likenessof data is a priori not known, but in the case of precipitation mapswavelike behaviour is evident as there are no hard angles or longstraight lines and the changes from one typical meteorologicallymeaningful timeframe to the next are small.

Embodiments described herein provide an efficient way of transmittingweather-related data or other suitable data to navigation devices, smartphones and any other suitable mobile devices over bandwidth limitedconnections. The embodiments can exploit properties of combined framesor other data sets rather than compressing or transmitting such framesor other data sets individually. The data can be transmitted to anymobile device with a screen enabling the viewing of weather image data,or to any other type of mobile device that has processing capacity toprocess the received data to extract information or make qualifieddecisions.

Whilst embodiments described in the foregoing detailed description referto GPS, it should be noted that the navigation device may utilise anykind of position sensing technology as an alternative to (or indeed inaddition to) GPS. For example the navigation device may utilise usingother global navigation satellite systems such as the European Galileosystem. Equally, embodiments are not limited to using satellite-basedsystems but could readily function using ground-based beacons, inertialsensors, or any other kind of system that enables the device todetermine its geographic location.

Whilst in embodiments described herein particular functionality isdescribed as being provided at a server and other functionality isdescribed as being provided at a device, for example at a PND or othermobile device, in alternative embodiments any of the describedfunctionality can be provided at either a server or at a device. Forexample substantially all of the functionality is provided at a serverin some embodiments, in which the server may operate as a navigationdevice. In other embodiments substantially all of the functionality isprovided at a device, which may receive weather or other data directlyfrom a source of such data rather than from the server.

Alternative embodiments of the invention can be implemented as acomputer program product for use with a computer system, the computerprogram product being, for example, a series of computer instructionsstored on a tangible data recording medium, such as a diskette, CD-ROM,ROM, or fixed disk, or embodied in a computer data signal, the signalbeing transmitted over a tangible medium or a wireless medium, forexample, microwave or infrared. The series of computer instructions canconstitute all or part of the functionality described above, and canalso be stored in any memory device, volatile or non-volatile, such assemiconductor, magnetic, optical or other memory device.

Whilst particular modules have been described herein, in alternativeembodiments functionality of one or more of those modules can beprovided by a single module or other component, or functionalityprovided by a single module can be provided by two or more modules orother components in combination.

It will also be well understood by persons of ordinary skill in the artthat whilst the preferred embodiment implements certain functionality bymeans of software, that functionality could equally be implementedsolely in hardware (for example by means of one or more ASICs(application specific integrated circuit)) or indeed by a mix ofhardware and software. As such, the scope of the present inventionshould not be interpreted as being limited only to being implemented insoftware.

It will be understood that the present invention has been describedabove purely by way of example, and modifications of detail can be madewithin the scope of the invention.

Each feature disclosed in the description, and (where appropriate) theclaims and drawings may be provided independently or in any appropriatecombination.

Lastly, it should also be noted that whilst the accompanying claims setout particular combinations of features described herein, the scope ofthe present invention is not limited to the particular combinationshereafter claimed, but instead extends to encompass any combination offeatures or embodiments herein disclosed irrespective of whether or notthat particular combination has been specifically enumerated in theaccompanying claims at this time.

1-54. (canceled)
 55. A mobile device, comprising: a communicationsresource for transmitting and receiving data, wherein the communicationsresource is configured to transmit location data identifying at leastone location and to receive a plurality of model parameter values inresponse to transmitted location data; and a processing resource forprocessing the received data, wherein the processing resource isconfigured to process the model parameter values to generate at leastone data set representing a value of a location-dependent variable for aplurality of locations across an area.
 56. The mobile device accordingto claim 55, wherein the processing resource is further configured toprocess the model parameter values to: generate a first data setrepresenting the value of the location-dependent variable for aplurality of locations across a first area at a first time; and generatea second data set representing the value of the location-dependentvariable for a plurality of locations across a second area at a secondtime.
 57. The mobile device according to claim 55, wherein the modelparameter values represent three dimensional data and at least some ofthe at least one data set is a two-dimensional data set representing aslice through the three dimensional data.
 58. The mobile deviceaccording to claim 55, further comprising: a display, wherein theprocessing resource is configured to generate an image or sequence ofimages on the display from the at least one data set.
 59. The mobiledevice according to claim 55, wherein the variable comprises aweather-related variable, the weather related variable beingrepresentative of expected weather conditions as a function of location.60. The mobile device according to claim 59, wherein the weatherconditions comprise one or more of: a presence or amount ofprecipitation; a wind speed and/or direction; a presence or amount oflying snow; a presence or amount of ice; a level of visibility; apresence or amount of fog; a temperature; and a presence or absence ofclouds.
 61. The mobile device according to claim 59, wherein thevariable is representative of an actual or expected modification inspeed due to the weather conditions.
 62. The mobile device accordingclaim 55, wherein the processing of the parameter values by theprocessing resource comprises determining the value of at least onefunction using the parameter values.
 63. The mobile device according toclaim 55, wherein the processing of the parameter values by theprocessing resource comprises performing an inverse transform using theparameter values
 64. The mobile device according to claim 64, whereinthe inverse transform comprises one of a Fourier transform or a wavelettransform.
 65. The mobile device according to claim 55, wherein thecommunications resource is operable to receive a plurality of sets ofparameter values, the processing resource is configured to extract arespective data set from each set of parameter values and combine thosedata sets to produce a combined data set.
 66. The mobile deviceaccording to claim 66, wherein each extracted data set represents animage for a respective different area, and the combining of the datasets by the processing resource comprises performing an image stitchingprocess for the images represented by the data sets.
 67. A system forproviding datasets representative of the variation of a parameter withlocation, comprising: a server system; and a mobile device, the mobiledevice comprising: a communications resource for transmitting andreceiving data, wherein the communications resource is configured totransmit location data identifying at least one location and to receivea plurality of model parameter values in response to transmittedlocation data; and a processing resource for processing the receiveddata, wherein the processing resource is configured to process the modelparameter values to generate at least one data set representing a valueof a location-dependent variable for a plurality of locations across anarea.
 68. The system according to claim 69, wherein the variablecomprises a weather-related variable, the weather related variable beingrepresentative of expected weather conditions as a function of location.69. The system according to claim 70, wherein the variable isrepresentative of an actual or expected modification in speed due to theweather conditions.
 70. A method of providing a data set to a mobiledevice, the method comprising: receiving, from the mobile device,location data identifying at least one location; in dependence on theidentified location, obtaining data that represents the value of aparameter as a function of location; processing the data to generate atleast one data set representative of the data, the processing comprisingdetermining, from the data, at least one contour that represents aboundary of a respective area for which the parameter has substantiallythe same value and generating contour data representing the at least onecontour, wherein the data set comprises the contour data; andtransmitting the data set to the mobile device.
 71. The method accordingto claim 72, wherein the parameter comprises a weather-relatedparameter, the weather related parameter being representative ofexpected weather conditions in a corresponding location.
 72. The methodaccording to claim 73, wherein the parameter comprises an actual orexpected modification in speed due to the weather conditions.
 73. Amethod of processing data at a mobile device, comprising: transmittinglocation data identifying at least one location; and receiving at leastone data set that represents a value of a parameter as a function oflocation in response to the transmitted location data, wherein the atleast one data set comprises contour data that represents at least onecontour, the or each contour representing a boundary of a respectivearea for which the parameter has substantially the same value; andprocessing the contour data to determine the value of the parameter forat least one location.
 74. The method according to claim 75, wherein theparameter comprises a weather-related parameter, the weather relatedparameter being representative of expected weather conditions in acorresponding location.
 75. The method according to claim 76, whereinthe parameter comprises an actual or expected modification in speed dueto the weather conditions.